HARDWARE FIT CHECK • Qwen 2.5 Coder 32B Instruct + AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)25.22 GB / 110 GB VRAM

Can I Run Qwen 2.5 Coder 32B Instruct on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 2.5 Coder 32B Instruct (32.5B) on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) (110 GB).

✓ FITS IN VRAM25.22 GB (23%)~7 t/s

Model & Target Hardware

interactive
The #1 Open-Source Coding Benchmark Model for 24GB–32GB GPUs
GPU Topology / Quantity:Single GPU / Unified SoC
Qwen 2.5 Coder 32B Instruct on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)

✓ Fits Comfortably in VRAM

✓ FITS IN VRAM
Required VRAM
25.22 GB
23% of hardware capacity (110 GB)
Est. Decode Speed
~7 tok/s
Dense model @ 256 GB/s
Available Headroom
+84.78 GB
Safe for OS display & full context
Memory Allocation & Headroom
25.22 GB allocated of 110 GB capacity
23% utilized
Weights: 19.5 GB
KV: (32K): 4.29 GB
Overhead: 1.43 GB
Free Headroom: +84.78 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~7 tok/s) is memory-bandwidth bound at batch size = 1 on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB) (256 GB/s). Prompt prefill / TTFT (~102 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run qwen2.5-coder:32b

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices
Consumer GPU
32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP
Consumer GPU
24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP
Multi-GPU Rig
48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8

936 GB/s700W TDP
Unified APU / Mini-PC
110 GB VRAM

AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)

Cheapest 128GB unified memory box for loading 70B–109B models

256 GB/s120W TDP

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw65 GB4.29 GB72.32 GB (66%)~2 t/sFITS
Q8_08.5 bpw34.45 GB4.29 GB40.7 GB (37%)~4 t/sFITS
Q6_K6.56 bpw26.65 GB4.29 GB32.62 GB (30%)~5 t/sFITS
Q5_K_M5.68 bpw23.07 GB4.29 GB28.92 GB (26%)~6 t/sFITS
Q4_K_M4.8 bpw19.5 GB4.29 GB25.22 GB (23%)~7 t/sFITS
FP44.25 bpw17.23 GB4.29 GB22.87 GB (21%)~8 t/sFITS
IQ3_XS3.3 bpw13.32 GB4.29 GB18.83 GB (17%)~10 t/sFITS

Export Full Architecture & Infrastructure Spec

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FAQ & Reference

Frequently Asked Questions: Qwen 2.5 Coder 32B Instruct on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)

How much VRAM does Qwen 2.5 Coder 32B Instruct need on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 Coder 32B Instruct requires 25.22 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Qwen 2.5 Coder 32B Instruct achieve on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)?

Based on AMD Strix Halo Mini-PC (Ryzen AI Max+ 395 128GB)'s 256 GB/s memory bandwidth, expected decode speed is approximately 7 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.